Browsing by Author "Shanthappa, N.K."

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Analyzing the Heterogeneity in Public Transit Demand: Impact of Spatial and Temporal Attributes
(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Venkateswari, N.P.
The usage of personal vehicles is causing several issues like traffic congestion, greenhouse gas emissions, and massive energy consumption. These issues can be alleviated by implementing a public bus transport system. But the irregular frequency of buses and longer waiting times are diminishing the attractiveness of public bus transportation in India. To implement an affordable and efficient public transport system, it is necessary to understand the heterogeneity ofÂ public transit demand under different conditions. Limited scholarly exploration on the impact of spatial and temporal characteristics on the heterogeneity of public transit demand under Indian conditions. This paper aims to measure public transit demand heterogeneity using the coefficient of variation of transit demand, which is defined as a ratio of standard deviation to mean. Spatial, temporal, and weather characteristics are considered to analyze their influence on the heterogeneity of public transit demand. The statistical variability analysis is performed using Electronic Ticketing Machine (ETM) data, weather data, and bus network data. The results indicate that the combined impact of weather conditions and the built environment has a stronger influence on the variability in Public Transit Demand (PTD) than each factor individually. Based on the analyses, it is recommended to change the service type to improve the transport system's efficiency. This study suggests the importance of incorporating spatial, temporal, and weather characteristics. This study can help stakeholders to optimize public transport networks and schedule service frequency. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Deep learning-based public transit passenger flow prediction model: integration of weather and temporal attributes
(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.
A reliable prediction model is critical for the public transit system to keep it periodically updated. However, it is a challenging task to develop a model of high precision when there is heterogeneity in the travel demand which is very common in developing countries. The spatial and temporal attributes along with external factors like weather should be incorporated into the prediction models to account for heterogeneity. Numerous studies in the past developed passenger flow prediction models considering spatial and temporal dependencies, whereas the integration of weather components with temporal dependencies while developing a prediction model for public bus transit has not been widely considered. Hence, the present research work employs long short-term memory (LSTM) to develop a route-level bus passenger flow prediction model, called RPTW-LSTM, by integrating temporal dependencies such as recent time intervals (R), daily periodicity (P) and weekly trend (T), and weather variables (W). The model is tested using a real-life dataset of the Udupi city bus service, located on the west coast of Karnataka, India. Additionally, Shapley Additive Explanation (SHAP) analysis is adopted to identify the relative importance of the features used. Results imply that the inclusion of the aforementioned factors enhanced the performance of RPTW-LSTM when compared to basic LSTM and other conventional models. Additionally, weekly trend and weather exhibit higher significance on the model than recent time intervals. This implies that evaluating the features affecting the heterogeneity in passenger flow and incorporating them into the model assists transport planners in achieving high precision. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
Effect of Weather on Passengers Access and Egress Mode Choice
(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Shukla, Y.
This study examines the impact of weather on public transportation, focusing on bus systems in Indian cities. Adverse weather events, such as intense rainfall and flooding, pose challenges to the efficiency and accessibility of bus services, affecting commuter satisfaction and operational reliability. With the projected increase in urban population and personal vehicle usage in India, thereâ€™s a pressing need to improve bus transit systems to alleviate traffic congestion, air pollution, and enhance urban quality of life. The problem addressed revolves around understanding how weather influences bus transit efficiency, especially during the last mile of passengersâ€™ commutes. The study aims to explore passenger perspectives, identify preferences, and address challenges faced during adverse weather conditions. The objective is to provide insights that inform targeted strategies to enhance the resilience and reliability of bus transit systems. The methodology involves conducting surveys and interviews with bus passengers in Indian cities to gather qualitative and quantitative data on their experiences and perceptions related to weather-induced disruptions. Analysis will identify common themes, preferences, and challenges, using statistical methods to uncover correlations and trends. The anticipated result of the study offers transportation authorities insights into passenger perspectives, aiding in the creation of targeted strategies to improve the resilience and reliability of bus transit systems during adverse weather. Ultimately, it seeks to contribute to the development of more passenger-centric and resilient public transportation systems in Indian cities. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Impact of Diverse Land Use and Population Densities on Access and Egress Mode Choice of Bus Transit System
(Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Sanjay, G.P.
This study investigates the impact of diverse land use patterns and population densities on commutersâ€™ mode choices for accessing and egressing public bus transit systems. The primary objectives of the study are to examine the influence of diverse land use patterns and population densities on mode choice, analyse the role of population density, explore user perceptions towards access and egress mode choices, and provide recommendations to improve service quality and transit ridership. The methodology involves spatial analysis, statistical modelling, and a detailed questionnaire survey to capture and analyse data. Mode choice behaviours are assessed through multinomial logistic regression models. User perceptions are gathered via surveys conducted across different urban areas with varying densities and land use patterns. Key findings indicate that areas with mixed-use developments and higher commercial density are associated with increased public transit usage, highlighting the importance of integrated urban planning in promoting sustainable transportation. The study also finds that higher population densities correlate positively with public transit use, demonstrating that densely populated areas benefit more from accessible and frequent bus services. Survey results reveal that convenience, travel time, and cost are primary factors influencing mode choice, with environmental concerns and safety also playing significant roles. These findings suggest that improving these aspects could enhance the attractiveness of public transit. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Origin-destination demand prediction of public transit using graph convolutional neural network
(Elsevier Ltd, 2024) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.
The insight into origin–destination (OD) demand patterns aids transport planners in making the public transit system more efficient and attractive. This may encourage individuals to shift from private vehicles to public transit, easing the burden on traffic and its negative impacts. Hence, to know how OD demand is going to vary in future, a state-of-the-art OD demand prediction model needs to be developed. Previously, studies have developed zone-based prediction models which may not be appropriate for predicting OD demand within a route of public transit. Additionally, spatial correlations between the stops of public transit must be included in the model for improved forecasting accuracy. Hence, in an effort to fulfil these gaps, a Graph Convolutional Neural Network (GCN) is developed to forecast the OD demand of public bus transit with nodes being the bus stops and links between them representing the passenger flow between the stops. Land use around the bus stops is retrieved as a node feature and included in the model to account for the spatial correlation between the stops. The model is trained using a real-life dataset from the public bus service of Davangere city located in India. Land use around the bus stops is extracted from the Davangere city master plan, procured from the urban development authority. The developed model is compared with conventional models and the findings show that the GCN model performs better in terms of prediction accuracy than the baseline models. Additionally, at the stop level, the performance of the model remained stable due to the inclusion of land use data compared to conventional models where land use data was not considered. © 2024 World Conference on Transport Research Society
The Spatiotemporal Patterns of Bus Passengers: Visualisation and Evaluation using Non-negative Tensor Decomposition
(Springer Nature, 2023) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.
Spatiotemporal analysis of passenger mobility patterns provides valuable information regarding the travel behaviour of passengers at different spatial and temporal scales. However, in the spatiotemporal analysis of passenger mobility patterns, a few questions are yet to be answered: how does passenger travel behaviour change during different seasons? In developing countries like India where land use distribution is complex, do travel characteristics have a relationship with spatial regions of different land use? And what is the influence of people from nearby sub-urban and villages on the passenger mobility of urban areas if transit service is provided? Hence, this study developed a methodology to visualise and analyse spatiotemporal variations in the bus passenger travel behaviour among different spatial regions at hourly, daily, and monthly temporal resolutions using non-negative tensor decomposition (NTD). Six-month electronic ticketing machine (ETM) data of the Davangere city bus service is collected. Land use data is also collected from the urban development authority of Davangere city. NTD was found efficient in extracting spatiotemporal patterns. From the analysis, it is observed that passenger mobility patterns across different spatial regions varied during different seasons and within a season as well. Pertaining to spatial variations, passenger origins and destinations are aggregated with respect to spatial regions with uniform land use or similar travel characteristics without giving any geographical inputs. Also, the mobility pattern of sub-urban and village people varied unconventionally. Thus, developed research methodology has the potential of unveiling the spatiotemporal variations in passenger mobility, which can act as a base for designing transit facilities and framing policies. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.